[leftmargin=*]
Detection of overlapping suspicious items by analyzing their predominant orientations across multiple scales within the candidate scan. Unlike [1]}, [2]}, [3]}, we propose a novel tensor pooling strategy to decompose the scan across various scales and fuses them via a single multi-scale tensor. This scheme results in more salient contour maps (see Figure REF ), boosting our framework's capacity for handling dulled, concealed, and overlapping items.

A thorough validation on three publicly available large-scale baggage X-ray datasets, including the OPIXray [4]}, which is the only dataset allowing a quantitative measure of the level of occlusion.

Unlike state-of-the-art methods such as CST [1]}, TST [2]}, and DTS [3]}, the performance of the proposed framework to detect occluded items has been quantitatively evaluated on OPIXray [4]} dataset. Please see Table REF  for more details.

